hgpu.org » GeForce RTX 2080 Ti
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
Tags: Biology, Chemistry, CUDA, GeForce RTX 2080 Ti, Machine learning, Molecular dynamics, Molecular simulation, Neural networks, nVidia, Package
January 23, 2022 by hgpu
Recent source codes
* * *
Most viewed papers (last 30 days)
- Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling
- 94% on CIFAR-10 in 3.29 Seconds on a Single GPU
- gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments
- Retargeting and Respecializing GPU Workloads for Performance Portability
- A Systematic Literature Survey of Sparse Matrix-Vector Multiplication
- Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs
- OpenMP offload at the Exascale using Intel GPU Max 1550: evaluation of STREAmS compressible solver
- QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays
- High Performance Privacy Preserving AI
- Python-Based Quantum Chemistry Calculations with GPU Acceleration
* * *